SHAP Value Explorer for Understanding Model Predictions

A model that predicts well but cannot say why quickly runs into trouble — users distrust it, stakeholders question it, and debugging it becomes guesswork. SHAP values have become the standard first answer to that problem, and this project gives an intern direct working experience with them on data they choose themselves.

The intern builds a Streamlit application where users upload a tabular dataset, train a simple scikit-learn model, and immediately explore SHAP explanation plots: summary plots ranking which features matter most overall, and per-prediction views showing how each feature pushed an individual output higher or lower. The app guides users through reading these plots correctly, turning SHAP from an intimidating acronym into a practical everyday tool, with Python handling the modeling pipeline end to end behind the interactive interface.

The project builds fluency in model interpretability — a topic employers increasingly treat as core data science rather than a specialty — and gives the intern practice at packaging an explanation workflow into an accessible tool, plus a portfolio piece showing they think critically about what models learn instead of just training them.